This notebook is part of smFRET burst analysis software FRETBursts.
This notebook shows how to implement Burst Variance Analysis (BVA) (Torella 2011) using FRETBursts.
For a complete tutorial on burst analysis see [FRETBursts - us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb).
We start loading the FRETBursts
software:
from fretbursts import *
sns = init_notebook()
- Optimized (cython) burst search loaded. - Optimized (cython) photon counting loaded. -------------------------------------------------------------- You are running FRETBursts (version 0.6.4). If you use this software please cite the following paper: FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 --------------------------------------------------------------
url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'
download_file(url, save_dir='./data')
URL: http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 File already on disk: /Users/anto/src/FRETBursts/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 Delete it to re-download.
file_name = "0023uLRpitc_NTP_20dT_0.5GndCl.hdf5"
# Here the folder is the subfolder "data" of current notebook folder
folder_name = './data/'
full_fname = folder_name + file_name
full_fname
'./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'
import os
if os.path.isfile(full_fname):
print ("Perfect, I found the file!")
else:
print ("Sorry, I can't find the file:\n", full_fname)
Perfect, I found the file!
d = loader.photon_hdf5(full_fname)
At this point, in d
, we only have the timestamps (ph_times_t
) and the detector numbers (det_t
):
d.add(det_donor_accept=(0, 1),
alex_period=4000,
D_ON=(2100, 3900),
A_ON=(150, 1900),
offset=700)
bpl.plot_alternation_hist (d)
loader.usalex_apply_period(d)
data_0023uLRpitc_NTP_20dT_0.5GndCl G1.000
Here we perform a standard burst search using the donor-excitation photon stream
(Ph_sel(Dex='DAem')
) as required for BVA.
Then, we apply a few selection filters to discard D-only and A-only bursts.
d.calc_bg(bg.exp_fit, time_s=50.1, tail_min_us='auto', F_bg=1.7)
- Calculating BG rates ... [DONE]
d.burst_search(m=10, computefret=False, ph_sel=Ph_sel(Dex='DAem'))
d.calc_fret(count_ph=True, corrections=False)
- Performing burst search (verbose=False) ... - Recomputing background limits for Dex ... [DONE] - Recomputing background limits for all ... [DONE] - Fixing burst data to refer to ph_times_m ... [DONE] [DONE] - Calculating burst periods ...[DONE]
ds = d.select_bursts(select_bursts.naa, th1=30, computefret=False)
ds1 = ds.select_bursts(select_bursts.size, th1=30, computefret=False)
ds_FRET = ds1.select_bursts(select_bursts.S, S1=0.25, S2=0.85, computefret=False)
dx=ds_FRET
alex_jointplot(dx)
<seaborn.axisgrid.JointGrid at 0x118599cc0>
We define a function to compute $s_E$:
def bva_sigma_E(n, bursts, DexAem_mask, out=None):
"""
Perform BVA analysis computing std.dev. of E for sub-bursts in each burst.
Split each burst in n-photons chunks (sub-bursts), compute E for each sub-burst,
then compute std.dev. of E across the sub-bursts.
For details on BVA see:
- Torella et al. (2011) Biophys. J. doi.org/10.1016/j.bpj.2011.01.066
- Ingargiola et al. (2016) bioRxiv, doi.org/10.1101/039198
Arguments:
n (int): number of photons in each sub-burst
bursts (Bursts object): burst-data object with indexes relative
to the Dex photon stream.
DexAem_mask (bool array): mask of A-emitted photons during D-excitation
periods. It is a boolean array indexing the array of Dex timestamps
(`Ph_sel(Dex='DAem')`).
out (None or list): append the result to the passed list. If None,
creates a new list. This is useful to accumulate data from
different spots in a single list.
Returns:
E_sub_std (1D array): contains for each burst, the standard deviation of
sub-bursts FRET efficiency. Same length of input argument `bursts`.
"""
E_sub_std = [] if out is None else out
for burst in bursts:
E_sub_bursts = []
startlist = range(burst.istart, burst.istop + 2 - n, n)
stoplist = [i + n for i in startlist]
for start, stop in zip(startlist, stoplist):
A_D = DexAem_mask[start:stop].sum()
assert stop - start == n
E = A_D / n
E_sub_bursts.append(E)
E_sub_std.append(np.std(E_sub_bursts))
return E_sub_std
Next we prepare the data for BVA:
ph_d = ds_FRET.get_ph_times(ph_sel=Ph_sel(Dex='DAem'))
bursts = ds_FRET.mburst[0]
bursts_d = bursts.recompute_index_reduce(ph_d)
Dex_mask = ds_FRET.get_ph_mask(ph_sel=Ph_sel(Dex='DAem'))
DexAem_mask = ds_FRET.get_ph_mask(ph_sel=Ph_sel(Dex='Aem'))
DexAem_mask_d = DexAem_mask[Dex_mask]
and call the bva_sigma_E
function:
n = 7
E_sub_std = bva_sigma_E(n, bursts_d, DexAem_mask_d)
Finally, we make a KDE plot of the 2D distribution E_sub_std
versus the burst FRET efficiency:
plt.figure(figsize=(6,6))
x = np.arange(0,1.01,0.01)
y = np.sqrt((x*(1-x))/n)
plt.plot(x,y, lw=3, color='red')
im = sns.kdeplot(ds_FRET.E[0], np.asfarray(E_sub_std), shade=True, cmap='viridis', shade_lowest=False)
plt.xlim(0,1)
plt.ylim(0,0.4)
plt.xlabel('E', fontsize=14)
plt.ylabel(r'$s_E$', fontsize=24);
Executed: Tue Jul 11 21:51:51 2017
Duration: 7 seconds.
Autogenerated from: [Example - Burst Variance Analysis.ipynb](out/Example - Burst Variance Analysis.ipynb)